Workload generation is employed for performance characterization, testing and benchmarking of computer systems dealing with processing, forwarding, storing and/or analysis of network traffic. Workload generation typically aims to simulate or emulate traffic generated by different types of applications, protocols and activities. For example, the activities might include email, chat, web browsing and traffic from sensor networks. The sensor networks might include video surveillance sensors, temperature monitoring sensors, and the like. Different approaches have been used for generating the traffic, such as model driven simulations and client-server architectures.
Examples of currently available traffic generation tools include commercial products such as LoadRunner, Netpressure, Http-Load, and MegaSIP; and academic prototypes such as SURGE, Wagon, Httperf, Harpoon, NetProbe, D-ITG, MGEN, and LARIAT.
The existing workload generation approaches focus primarily on matching predetermined volumetric and timing properties, and ignore statistical properties at the content level, such as content and contextual semantics. Most of the existing approaches for traffic generation are application specific or lack scalability and/or modularity. The traffic generated by these approaches is not suitable for testing and benchmarking systems that analyze data content and make intelligent decisions based on the content. The majority of these tools are not content based or generate only a limited level of content and contextual richness.